Table 2.

Summary of interpretability of CDSS from the technological perspective.

Ref.	CDSS based on AI	Interpretation method	CDSS type
Saak et al. [7]	Lasso regression, elastic nets random forests	Feature importance	DB-CDSS White box
Raja and Asghar [49]	Cooperative Bayesian game-theoretic	Fuzzy genetics	DB-CDSS White box
Liu et al. [4]	Multiclass logistic regression	Feature importance	DB-CDSS White box
Vandewiele et al. [22]	Decision tree, annotated with expert knowledge base	Decision tree	DB-CDSS White box
Liu et al. [30]	Decision tree	Decision tree visualization dashboard	DB-CDSS White box
McRae et al. [29]	Cardiac score card based on lasso logistic regression	Lasso logistic regression model	DB-CDSS White box
Esmaeili et al. [8]	Random forest, Naïve Bayes, K-NN, and deep learning	Sensitivity analysis	DB-CDSS
Rho et al. [50]	Random forest k-nearest neighbors logistic regression	Interface (visualization)	DB-CDSS
Kaji et al. [9]	Deep learning long short-term memory recurrent neural networks (RNNs)	Activation maximization	DB-CDSS
Kermany et al. [15]	Neural network	Feature importance	DB-CDSS
Tolonen et al. [51]	DSI classifier, RUSBoost	Visualization	DB-CDSS
Billiet et al. [20]	DSI classifier combination of complex machine learning	Interval coded scoring with toolbox interface (visualization)	DB-CDSS
Tsao et al. [52]	Decision trees, support vector machines, artificial neural networks, and logistic regressions	Features weights	DB-CDSS
Gaw et al. [3]	LDA, QDA, and SVM (LSVM)	Sensitivity analysis (biomarker identification)	DB-CDSS
Müller et al. [2]	Bayesian for inferring and ranking possible diagnoses	Bayesian visualization	KB-CDSS
El-Sappagh et al. [19]	Ontology-fuzzy rule-based	Fuzzy logic	KB-CDSS
Jabez Christopher et al. [53]	Decision rule	Decision rule	KB-CDSS
Wagholikar et al. [17]	Free-text rule base and guideline rule base	Decision rule	KB-CDSS
Esposito et al. [54]	Fuzzy logic	Fuzzy logic	KB-CDSS
Kuo and Fuh [55]	Decision rule	Decision rule	KB-CDSS